Experienced GIS Analyst and Geospatial Data Specialist with expertise in spatial analysis, mapping, and data automation to support environmental planning, infrastructure projects, and scientific research. Proficient in ArcGIS, QGIS, Python, R, and SQL, with a strong background in ecological analysis, spatial modeling, and geospatial data visualization. Skilled in managing GIS databases, optimizing workflows, and integrating geospatial insights into project planning and decision-making. Can engage stakeholders in non-technical explanations of complex processes.
A comparison of Maxent’s default settings with two optimal model selection techniques for a species with few known localities, in both high and low sampling bias environments.
Finding that karst topography is an apparent abiotic driver of François’ langur distribution projected into multiple climate scenarios.
Projecting distribution models created from modern occurrence localities into the past to examine how the isthmus of Panama may have led to speciation among Central American bird species.
Home range utilization of resident green turtles in a Marine Protected Area.
Naro-Maciel, E., Arengo, F., Galante, P., Vintinner, E., Holmes, K. E., Balazs, G., & Sterling, E. J. (2018). Marine protected areas and migratory species: residency of green turtles at Palmyra Atoll, Central Pacific. Endangered Species Research, 37, 165-182.ENMeval: An R package for automated Maxent model tuning across a range of settings with options for reducing sampling bias and data-partitioning.
Muscarella, R., Galante, P. J., Soley‐Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M., & Anderson, R. P. (2014). ENM eval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in ecology and evolution, 5(11), 1198-1205.Wallace 2: A graphical user interface with an updated version of wallace with faster processing, 9 new modules, and added functionalities for data acquisition, metadata tracking, and citations.
maskRangeR: Incorporating expert knowledge with models of species’ distributions can increase accuracy of range estimates.
ENMeval version 2: with user-suggested improvements with faster model evaluations with an R-native maxent algorithm.
changeRangeR: Reproducibly transform estimates of species’ distributions into metrics relevant for conservation.